Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models by Basim Mahbooba, Radhya Sahal, Wael Alosaimi, Martin Serrano

“…The four machine learning techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), and naïve Bayes (NB). …”
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Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models by Dmytro Chumachenko, Tetiana Dudkina, Sergiy Yakovlev, Tetyana Chumachenko

“…Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. …”
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Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning by Cheng Wang, Cheng Wang, Cheng Wang, Zhou Long, Zhou Long, Xiang-Dong Wang, Xiang-Dong Wang, You-Qi Kong, Li-Chun Zhou, Wei-Hua Jia, Pei Li, Jing Wang, Xiao-Juan Wang, Tian Tian

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Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females by Sara A. Heyn, Taylor J. Keding, Josh Cisler, Katie McLaughlin, Ryan J. Herringa

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Prediction of High-Pressure Physical Properties of Crude Oil Using Explainable Machine Learning Models by Yawen He, Jianwen Chen, Zhiyu Wu, Yaxin Dun, Guichao Du, Mengsen Feng, Yifan Lu, Wei Dang

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CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT FROM SCIENTIFIC ECHOSOUNDER DATA: APPLICATION OF MACHINE LEARNING ALGORITHMS by Baigo HAMUNA, Sri PUJIYATI, Jonson Lumban GAOL, Totok HESTIRIANOTO

“…The classification and prediction process of benthic habitats uses two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), in XLSTAT Basic+ software. …”
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Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments by Koji Hashimoto, Koshiro Matsuo, Masaki Murata, Gakuto Ogiwara, Daichi Takeda

Subjects: “…machine-learning…”
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Classical and machine learning tools for identifying yellow-seeded Brassica napus by fusion of hyperspectral features by Fan Liu, Fang Wang, Zaiqi Zhang, Liang Cao, Jinran Wu, You-Gan Wang

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Large language models for chart review: how machine learning can accelerate hematology research by Barbara D. Lam, Peiqi Wang, Shengling Ma, Omid Jafari, Iuliia Kovalenko, Ang Li

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Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning by Despoina P. Kiouri, Georgios C. Batsis, Christos T. Chasapis

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The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning by Yuchen Lu, Jiakun Zhuang, Jun Chen, Chenlu Yang, Mei Kong

“…The analysis of the impact of the 2010 liberalisation of the land transfer policy employs a dual machine learning model, utilising provincial-level data from China spanning 2005 to 2022, to address the limitations of traditional causal inference models while ensuring estimation accuracy. …”
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Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact by Enrique Mondragon-Estrada, Jane W. Newburger, Steven R. DePalma, Martina Brueckner, John Cleveland, Wendy K. Chung, Bruce D. Gelb, Elizabeth Goldmuntz, Donald J. Hagler, Jr., Hao Huang, Patrick McQuillen, Thomas A. Miller, Ashok Panigrahy, George A. Porter, Jr., Amy E. Roberts, Caitlin K. Rollins, Mark W. Russell, Martin Tristani-Firouzi, P. Ellen Grant, Kiho Im, Sarah U. Morton

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An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease by Md. Jakir Hossen, Thirumalaimuthu Thirumalaiappan Ramanathan, Abdullah Al Mamun

“…The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.…”
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A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges by Zongxu Liu, Hui Guo, Yingshuai Zhang, Zongliang Zuo

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Improving Electron Density Predictions in the Topside of the Ionosphere Using Machine Learning on In Situ Satellite Data by S. Dutta, M. B. Cohen

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Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques by Anjali Jaglan, Rati Ram Singh

“…In this study, Response Surface Methodology (RSM) and machine learning models were used to predict the mechanical properties of recycled aggregate concrete (RAC) containing ground granulated blast furnace slag (GGBS) and polypropylene fibers (PPFs). …”
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On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces by Yingkun Zhu, Haomin Song, Ruiying Liu, Yunyun Mu, Murali Gedda, Abdullah N. Alodhay, Lei Ying, Qiaoqiang Gan

“…Our study introduces a novel approach for on-site quantitative detection of fentanyl in heroin, employing machine learning-enabled surface-enhanced Raman spectroscopy (SERS) on superabsorbing metasurfaces. …”
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Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning by Stefanie Steinbach, Anna Bartels, Andreas Rienow, Bartholomew Thiong’o Kuria, Sander Jaap Zwart, Andrew Nelson

“…Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. …”
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A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States by Sajeev Magesh, Kevin Geng

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Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach by Sajadul Hassan Kumhar, Prabhakara Rao Kapula, Harveen Kaur, Radeep R. Krishna, Mudasir M Kirmani, Vijay Anant Athavale, Mohd Wazih Ahmad

“…Scientists are trying to find a solution using some machine learning (ML) techniques. The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. …”
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